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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code>.NeighborhoodComponentsAnalysis</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-neighbors-neighborhoodcomponentsanalysis">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.NeighborhoodComponentsAnalysis</span></code></a></li>
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  <div class="section" id="sklearn-neighbors-neighborhoodcomponentsanalysis">
<h1><a class="reference internal" href="../classes.html#module-sklearn.neighbors" title="sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code></a>.NeighborhoodComponentsAnalysis<a class="headerlink" href="#sklearn-neighbors-neighborhoodcomponentsanalysis" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.neighbors.NeighborhoodComponentsAnalysis">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.neighbors.</code><code class="sig-name descname">NeighborhoodComponentsAnalysis</code><span class="sig-paren">(</span><em class="sig-param">n_components=None</em>, <em class="sig-param">init='auto'</em>, <em class="sig-param">warm_start=False</em>, <em class="sig-param">max_iter=50</em>, <em class="sig-param">tol=1e-05</em>, <em class="sig-param">callback=None</em>, <em class="sig-param">verbose=0</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_nca.py#L30"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis" title="Permalink to this definition">¶</a></dt>
<dd><p>Neighborhood Components Analysis</p>
<p>Neighborhood Component Analysis (NCA) is a machine learning algorithm for
metric learning. It learns a linear transformation in a supervised fashion
to improve the classification accuracy of a stochastic nearest neighbors
rule in the transformed space.</p>
<p>Read more in the <a class="reference internal" href="../neighbors.html#nca"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_components</strong><span class="classifier">int, optional (default=None)</span></dt><dd><p>Preferred dimensionality of the projected space.
If None it will be set to <code class="docutils literal notranslate"><span class="pre">n_features</span></code>.</p>
</dd>
<dt><strong>init</strong><span class="classifier">string or numpy array, optional (default=’auto’)</span></dt><dd><p>Initialization of the linear transformation. Possible options are
‘auto’, ‘pca’, ‘lda’, ‘identity’, ‘random’, and a numpy array of shape
(n_features_a, n_features_b).</p>
<dl class="simple">
<dt>‘auto’</dt><dd><p>Depending on <code class="docutils literal notranslate"><span class="pre">n_components</span></code>, the most reasonable initialization
will be chosen. If <code class="docutils literal notranslate"><span class="pre">n_components</span> <span class="pre">&lt;=</span> <span class="pre">n_classes</span></code> we use ‘lda’, as
it uses labels information. If not, but
<code class="docutils literal notranslate"><span class="pre">n_components</span> <span class="pre">&lt;</span> <span class="pre">min(n_features,</span> <span class="pre">n_samples)</span></code>, we use ‘pca’, as
it projects data in meaningful directions (those of higher
variance). Otherwise, we just use ‘identity’.</p>
</dd>
<dt>‘pca’</dt><dd><p><code class="docutils literal notranslate"><span class="pre">n_components</span></code> principal components of the inputs passed
to <a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> will be used to initialize the transformation.
(See <a class="reference internal" href="sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">PCA</span></code></a>)</p>
</dd>
<dt>‘lda’</dt><dd><p><code class="docutils literal notranslate"><span class="pre">min(n_components,</span> <span class="pre">n_classes)</span></code> most discriminative
components of the inputs passed to <a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> will be used to
initialize the transformation. (If <code class="docutils literal notranslate"><span class="pre">n_components</span> <span class="pre">&gt;</span> <span class="pre">n_classes</span></code>,
the rest of the components will be zero.) (See
<a class="reference internal" href="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearDiscriminantAnalysis</span></code></a>)</p>
</dd>
<dt>‘identity’</dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">n_components</span></code> is strictly smaller than the
dimensionality of the inputs passed to <a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>, the identity
matrix will be truncated to the first <code class="docutils literal notranslate"><span class="pre">n_components</span></code> rows.</p>
</dd>
<dt>‘random’</dt><dd><p>The initial transformation will be a random array of shape
<code class="docutils literal notranslate"><span class="pre">(n_components,</span> <span class="pre">n_features)</span></code>. Each value is sampled from the
standard normal distribution.</p>
</dd>
<dt>numpy array</dt><dd><p>n_features_b must match the dimensionality of the inputs passed to
<a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> and n_features_a must be less than or equal to that.
If <code class="docutils literal notranslate"><span class="pre">n_components</span></code> is not None, n_features_a must match it.</p>
</dd>
</dl>
</dd>
<dt><strong>warm_start</strong><span class="classifier">bool, optional, (default=False)</span></dt><dd><p>If True and <a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> has been called before, the solution of the
previous call to <a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> is used as the initial linear
transformation (<code class="docutils literal notranslate"><span class="pre">n_components</span></code> and <code class="docutils literal notranslate"><span class="pre">init</span></code> will be ignored).</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, optional (default=50)</span></dt><dd><p>Maximum number of iterations in the optimization.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, optional (default=1e-5)</span></dt><dd><p>Convergence tolerance for the optimization.</p>
</dd>
<dt><strong>callback</strong><span class="classifier">callable, optional (default=None)</span></dt><dd><p>If not None, this function is called after every iteration of the
optimizer, taking as arguments the current solution (flattened
transformation matrix) and the number of iterations. This might be
useful in case one wants to examine or store the transformation
found after each iteration.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int, optional (default=0)</span></dt><dd><p>If 0, no progress messages will be printed.
If 1, progress messages will be printed to stdout.
If &gt; 1, progress messages will be printed and the <code class="docutils literal notranslate"><span class="pre">disp</span></code>
parameter of <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize" title="(in SciPy v1.4.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.optimize.minimize</span></code></a> will be set to
<code class="docutils literal notranslate"><span class="pre">verbose</span> <span class="pre">-</span> <span class="pre">2</span></code>.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int or numpy.RandomState or None, optional (default=None)</span></dt><dd><p>A pseudo random number generator object or a seed for it if int. If
<code class="docutils literal notranslate"><span class="pre">init='random'</span></code>, <code class="docutils literal notranslate"><span class="pre">random_state</span></code> is used to initialize the random
transformation. If <code class="docutils literal notranslate"><span class="pre">init='pca'</span></code>, <code class="docutils literal notranslate"><span class="pre">random_state</span></code> is passed as an
argument to PCA when initializing the transformation.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>components_</strong><span class="classifier">array, shape (n_components, n_features)</span></dt><dd><p>The linear transformation learned during fitting.</p>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">int</span></dt><dd><p>Counts the number of iterations performed by the optimizer.</p>
</dd>
<dt><strong>random_state_</strong><span class="classifier">numpy.RandomState</span></dt><dd><p>Pseudo random number generator object used during initialization.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="rf9b6baee8229-1"><span class="brackets">Rf9b6baee8229-1</span></dt>
<dd><p>J. Goldberger, G. Hinton, S. Roweis, R. Salakhutdinov.
“Neighbourhood Components Analysis”. Advances in Neural Information
Processing Systems. 17, 513-520, 2005.
<a class="reference external" href="http://www.cs.nyu.edu/~roweis/papers/ncanips.pdf">http://www.cs.nyu.edu/~roweis/papers/ncanips.pdf</a></p>
</dd>
<dt class="label" id="rf9b6baee8229-2"><span class="brackets">Rf9b6baee8229-2</span></dt>
<dd><p>Wikipedia entry on Neighborhood Components Analysis
<a class="reference external" href="https://en.wikipedia.org/wiki/Neighbourhood_components_analysis">https://en.wikipedia.org/wiki/Neighbourhood_components_analysis</a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NeighborhoodComponentsAnalysis</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_iris</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span><span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nca</span> <span class="o">=</span> <span class="n">NeighborhoodComponentsAnalysis</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">NeighborhoodComponentsAnalysis(...)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">knn</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">knn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">KNeighborsClassifier(...)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">knn</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span>
<span class="go">0.933333...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">knn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">nca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">),</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">KNeighborsClassifier(...)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">knn</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">nca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> <span class="n">y_test</span><span class="p">))</span>
<span class="go">0.961904...</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y)</p></td>
<td><p>Fit the model according to the given training data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.transform" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Applies the learned transformation to the given data.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.neighbors.NeighborhoodComponentsAnalysis.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_components=None</em>, <em class="sig-param">init='auto'</em>, <em class="sig-param">warm_start=False</em>, <em class="sig-param">max_iter=50</em>, <em class="sig-param">tol=1e-05</em>, <em class="sig-param">callback=None</em>, <em class="sig-param">verbose=0</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_nca.py#L162"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_nca.py#L174"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model according to the given training data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>The training samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like, shape (n_samples,)</span></dt><dd><p>The corresponding training labels.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>returns a trained NeighborhoodComponentsAnalysis model.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L544"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array of shape [n_samples]</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">numpy array of shape [n_samples, n_features_new]</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NeighborhoodComponentsAnalysis.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_nca.py#L242"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the learned transformation to the given data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Data samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>X_embedded: array, shape (n_samples, n_components)</dt><dd><p>The data samples transformed.</p>
</dd>
</dl>
</dd>
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><dl class="simple">
<dt>NotFittedError</dt><dd><p>If <a class="reference internal" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> has not been called before.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-neighbors-neighborhoodcomponentsanalysis">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.NeighborhoodComponentsAnalysis</span></code><a class="headerlink" href="#examples-using-sklearn-neighbors-neighborhoodcomponentsanalysis" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="An illustration of various embeddings on the digits dataset."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_lle_digits_thumb.png" src="../../_images/sphx_glr_plot_lle_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py"><span class="std std-ref">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates a learned distance metric that maximizes the nearest neighbors classif..."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_nca_illustration_thumb.png" src="../../_images/sphx_glr_plot_nca_illustration_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_nca_illustration.html#sphx-glr-auto-examples-neighbors-plot-nca-illustration-py"><span class="std std-ref">Neighborhood Components Analysis Illustration</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example comparing nearest neighbors classification with and without Neighborhood Components ..."><div class="figure align-default" id="id5">
<img alt="../../_images/sphx_glr_plot_nca_classification_thumb.png" src="../../_images/sphx_glr_plot_nca_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py"><span class="std std-ref">Comparing Nearest Neighbors with and without Neighborhood Components Analysis</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Sample usage of Neighborhood Components Analysis for dimensionality reduction."><div class="figure align-default" id="id6">
<img alt="../../_images/sphx_glr_plot_nca_dim_reduction_thumb.png" src="../../_images/sphx_glr_plot_nca_dim_reduction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py"><span class="std std-ref">Dimensionality Reduction with Neighborhood Components Analysis</span></a></span><a class="headerlink" href="#id6" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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